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- September
Boulder, Colorado, USA
nd
International Satellite Snow
Products Intercomparison
Workshop
nd
ISSPI-
International Satellite Snow Products Intercomparison
Workshop
Program
SCIENTIFIC COMMITTEE
Gabriele Bippus (ENVEO)
Bojan Bojkov (ESA)
Chris Derksen (EC)
Richard Fernandes (CCRS)
Dorothy Hall (NASA)
Sean Helfrich (NOAA)
Kari Luojus (FMI, WMO GCW)
Sari Metsämäki (SYKE)
Thomas Nagler (ENVEO) (Chair)
Jouni Pulliainen (FMI)
Dave Robinson (Rutgers University)
Helmut Rott (ENVEO)
Rune Solberg (NR)
Marco Tedesco (CCNY/CUNY)
VENUE
Aspen Room
University Memorial Center (UMC)
University of Colorado Boulder 80302
Boulder, CO, USA
DATE
-
September
SNOWPEX PROJECT AND WORKSHOP WEBSITE
Updated information and outcome of the workshop is made available at
http://calvalportal.ceos.org/projects/snowpex.
LOCAL ORGANIZER
Cindy Brekke / NSIDC
CONTACTS
Thomas Nagler (thomas.nagler@enveo.at)
Bojan Bojkov (bojan.bojkov@esa.int)
The Satellite Snow Products Intercomparison
and Evaluation Experiment
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Program
International Satellite Snow Products Intercomparison
Workshop
ISSPI-
DIRECTIONS
Venue:
Aspen Room
University Memorial Center (UMC)
University of Colorado Boulder 80302
We recommend that participants walk to the facility rather than drive.
If you do wish to drive there is parking available to the east of the UMC at the Euclid AutoPark lot. You will need to have
your license plate number to pay at any of the pay-stations located at each pedestrian exit.
The EAP parking rate is $2.00 per hour up to three hours, during the hours of 7:30 AM - 5:00 PM. After three hours, the rate
is $4.00 per hour. The parking fee is $4.00 from 5:00 p.m. until 7:30 a.m. each weeknight and all day Saturday and Sunday.
EAP pay-stations accept payment by credit card, coin and Parkmobile (Zone 6334.)
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The Satellite Snow Products Intercomparison
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International Satellite Snow Products Intercomparison
Workshop
Program
Walking Directions from the Boulder Inn to the UMC
The Satellite Snow Products Intercomparison
and Evaluation Experiment
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Program
International Satellite Snow Products Intercomparison
Workshop
ISSPI-
PROGRAM
MONDAY,
:
:
MON-
September
- MORNING
RECEPTION
ORAL SESSION: OPENING
Welcome
Mon 1.1
:
:
Housekeeping and organisation
Chair: Thomas Nagler
Chris Derksen
Thomas Nagler (ENVEO), Bojan Bojkov
(ESA) /
Cindy Brekke / NSIDC
Mon 1.2
:
:
The Satellite Snow Product Intercomparison and Evaluation
Experiment SnowPEx: Objectives, status and expected
results
Thomas Nagler
Mon 1.3
:
:
On the importance of fiducial reference measurements for
EO satellite characterisation and traceability
Bojan Bojkov
ORAL SESSION: SNOW EXTENT PRODUCTS -
Chair: Thomas Nagler
Chris Derksen
Mon 1.4
:
:
Overview of snow extent products participating at SnowPEx
and pre-processing steps for intercomparison
Gabriele Bippus, Thomas Nagler,
Elisabeth Ripper, Richard Fernandes,
Sari Metsämäki
Mon 1.5
:
:
Intercomparison of snow extent products - CCRS method
and first results
Richard Fernandes, Gabriele Bippus,
Sari Metsämäki, Elisabeth Ripper,
Thomas Nagler
:
:
COFFEE BREAK
ORAL SESSION: SNOW EXTENT PRODUCTS –
Chair: Richard Fernandes
David Robinson
:
Intercomparison of snow extent products - ENVEO method
and first results
Gabriele Bippus, Elisabeth Ripper,
Thomas Nagler
Elisabeth Ripper, Gabriele Bippus,
Thomas Nagler, Christopher J.
Crawford, Sari Metsämäki, Rune
Solberg, and Karl Rittger
MONMon- .
:
Mon- .
:
:
Landsat based snow extent reference data set and first
validation results
Mon- .
:
:
SnowPEx in-situ data sets and first validation results
Sari Metsämäki, Gabriele Bippus, Kari
Luojus, Chris Derksen, et al.
Mon- .
:
:
NESDIS Global Automated Satellite Snow Product: current
status and planned upgrades
Peter Romanov
Mon- .
:
:
Recent advancements to the Interactive Multisensor Snow
and Ice Mapping System
Sean Helfrich
:
:
LUNCH BREAK
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MONDAY,
MON-
September
Program
- AFTERNOON
ORAL SESSION: SNOW EXTENT PRODUCTS –
Chair: George Riggs
Rune Solberg
Mon- .
:
:
Standard MODIS snow cover products
George A. Riggs, Dorothy K. Hall,
Christopher A. Crawford, and Nicolo E.
DiGirolamo
Mon- .
:
:
Evaluation of JXAM5 snow cover extent product using
ground based snow depth information and Landsat images
Masahiro Hori, Konosuke Sugiura,
Tomonori Tanikawa, Teruo Aoki,
Masashi Niwano, and Hiroyuki Enomoto
Mon- .
:
:
Assessment of H-SAF MSG/SEVIRI snow cover product over
the UK
Samantha Pullen
Mon- .
:
:
Assessment of the stability of a satellite snow extent CDR
from station snow depth observations
David Robinson, Thomas Mote, and Kim
Love-Myers
Mon- .
:
:
Time series analysis of mountain snow cover from
MODSCAG and VIIRSCAG
Kathryn Bormann, Karl Rittger, Thomas.
H. Painter
:
:
COFFEE BREAK
ORAL SESSION: SNOW EXTENT PRODUCTS -
Chair: Gabriele Bippus
Karl Rittger
Advancements and validation of the global CryoClim snow
cover extent product
Rune Solberg, Øystein Rudjord, ArntBørre Salberg, Mari Anne Killie
POSTER PRESENTATIONS: 2 Minutes (1-2 Slides)
Chair: Gabriele Bippus
Karl Rittger
Validating a new snow cover extent mapping algorithm
over Eastern Canada
Sophie Roberge, Karem Chokmani, and
Danielle De Sève
Mon- .
Satellite based probabilistic snow cover extent mapping
(SCE) at Hydro-Québec
Danielle De Seve, Teasdale Mylène,
Jean-Francois Anger, and Luc Perreault
Mon- .
Fractional snow cover for Landsat OLI, MODIS, and VIIRS
from spectral mixture analysis
Karl Rittger, Kathryn Bormann, Richard,
Armstrong, Thomas, Painter, and Jeff
Dozier
Mon- .
Scene-specific fractional snow cover algorithms for optical
remote sensing
Igor Appel
Mon- .
The seasonality of snow/cloud discrimination in Landsat
imagery across the Northern Hemisphere
Chris Crawford
Mon- .
Evaluating multi-spectral snowpack reflectivity with
changing snow grain sizes
Do Hyuk Kang, Ana Barros, and Edward
Kim
Mon- .
Optimal linear relationships of fractional snow cover with
visible reflectance and NDSI
Igor Appel
Mon- .
Snow monitoring in mountain areas from medium to high
resolution images: towards Sentinel 2 data
Carlo Marin, Edoardo Cremonese,
Ludovica De Gregorio, Mattia Callegari,
and Claudia Notarnicola
MON-
Mon- .
:
MON-
Mon- .
:
:
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Program
Hiroyuki Tsutsui, Keiji Imaoka, Richard
Kelly, and Takashi Maeda
Using image reconstruction to enhance spatial resolution of
a reprocessed satellite passive microwave historical record
Mary J. Brodzik, David L. Long, Molly A.
Hardman, Aaron Paget, and Richard L.
Armstrong
:
COST Action ES1404: A European network for a harmonised
monitoring of snow for the benefit of climate change
scenarios, hydrology and numerical weather prediction
Samantha Pullen
:
ICEBREAKER AND POSTER SESSION
Mon.
:
ISSPI-
Updated JAXA GCOM-W/AMSR2 snow depth standard
product and its validation
Mon- .
Mon.
International Satellite Snow Products Intercomparison
Workshop
TUESDAY,
TUE-
September
- MORNING
ORAL SESSION: SNOW WATER EQUIVALENT-
Chair: Chris Derksen
Carrie Vuyovich
Tue- .
:
:
The Satellite Snow Product Intercomparison and Evaluation
Experiment: overview of progress for snow water
equivalent
Chris Derksen, Lawrence Mudryk, Kari
Luojus, Ross Brown, and Carrie
Vuyovich
Tue- .
:
:
Assessment of the satellite-based SWE datasets with snow
transect data
Kari Luojus, Jouni Pulliainen, Chris
Derksen, Ross Brown
Tue- .
:
:
Evaluating the consistency and accuracy of several
Northern Hemisphere snow water equivalent datasets
Lawrence Mudryk, Chris Derksen, Paul
Kushner, and Ross Brown
Tue- .
:
:
Development of a 10-km gridded SWE dataset over southwestern Quebec for SnowPEx validation
Ross Brown and Dominique Tapsoba
Tue- .
:
:
Watershed-based comparison of global SWE algorithms to
SNODAS modelled SWE across the United States
Carrie Vuyovich, Jennifer Jacobs,
Timothy Baldwin, and Steven Daly
:
:
COFFEE BREAK
TUE-
ORAL SESSION: SNOW WATER EQUIVALENT-
Chair: Kari Luojus
Lawrence Mudryk
Tue- .
:
:
An Overview of the current NASA operational AMSRE/AMSR2 snow science team activities
Marco Tedesco, Mattia Sartori, and
Jeyavinoth Jeyaratnam
Tue- .
:
:
The GCOM-W1 AMSR2 snow depth and snow water
equivalent product
Richard Kelly, Nastaran Saberi, and
Qinghuan Li
Tue- .
:
:
Contributions of SnowPEx to Global Climate Modeling
Initiatives
Chris Derksen, with contributions from
the LS3MIP and ESM-SnowMIP steering
committees
Tue- .
:
:
The NASA SnowEx airborne snow campaign: an upcoming
opportunity for SnowPEx
Edward Kim, Charles Gatebe, Dorothy
Hall, and Matthew Sturm
:
:
LUNCH BREAK
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The Satellite Snow Products Intercomparison
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International Satellite Snow Products Intercomparison
Workshop
TUESDAY,
TUE-
September
- AFTERNOON
SESSION: SPLINTER- SE & SWE Separately
:
:
Snow Extent: Topic: Review of generation of reference data
/ Pre-processing for Intercomparisons
Chair: Thomas Nagler
:
:
Snow Water Equivalent: Review of results; upcoming
comparisons with in situ data
Chair: Chris Derksen
:
:
COFFEE BREAK
TUE-
Rapporteur: Richard Fernandes
Rapporteur: Kari Luojus
SESSION: SPLINTER- SE & SWE Separately
:
:
Snow Extent: Intercomparison and validation protocols:
discussion, trend analysis, and next steps
:
:
Snow Water Equivalent: plans for trend analysis
WEDNESDAY,
WED1.1
Program
:
:
WED-
September
Chair: Thomas Nagler
Rapporteur: Richard Fernandes
Chair: Chris Derksen
Rapporteur: Kari Luojus
- MORNING
The role of product intercomparisons in the Global
Cryosphere Watch
Jeff Key
SESSION: SPLINTER-3 SE & SWE Separately
:
:
Snow Extent (continued)
:
:
Snow Water Equivalent (continued)
:
:
COFFEE BREAK
WED-
Chair: Thomas Nagler
Rapporteur: Richard Fernandes
Chair: Chris Derksen
Rapporteur: Kari Luojus
ORAL SESSION: SUMMARY
Chair: Thomas Nagler
Chris Derksen
WED .
:
:
Summary of SE Splinter Session
Thomas Nagler, Richard Fernandes
WED .
:
:
Summary of SWE Splinter Session
Chris Derksen, Kari Luojus
WED .
:
:
Discussion
Chris Derksen / Thomas Nagler
WED .
:
:
Action Items, Next Steps
Thomas Nagler / Chris Derksen
Closing of Workshop
Thomas Nagler (ENVEO),
Bojan Bojkov (ESA)
WED-
:
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ABSTRACTS
MON-1
MONDAY, SEPTEMBER
ORAL SESSION: OPENING
Mon- .
The Satellite Snow Product Intercomparison and Evaluation Experiment SnowPEx:
Objectives, status and expected results
Thomas Nagler
ENVEO IT GmbH, Austria
This presentation will provide an overview and current status of the SnowPEx Initiative to the Workshop participants. The
motivation and the primary objectives of the project will be presented and the overall design of the project will be
explained. The progress of the 1st year of the SnowPEx project will be summarized. An overview on the protocols and
validation methods for snow extent and snow water equivalent methods including selected examples will be given pointing
to the detailed presentations at the WS. The available reference data set consisting of in-situ snow maps and Landsat data
will be summarized. The expected results from the second international snow product intercomparison workshop will be
presented as well as the overall outcome of the project.
The Satellite Snow Products Intercomparison
and Evaluation Experiment
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Program
Mon- .
International Satellite Snow Products Intercomparison
Workshop
ISSPI-
On the importance of fiducial reference measurements for EO satellite characterisation
and traceability
Bojan R. Bojkov
European Space Agency (ESA), Italy
Earth observation (EO) from satellites has revolutionised our view of the Earth s atmosphere, oceans, land and the
cryosphere. Due to these advances, EO offers an enormous potential for continuous monitoring the state of the
environment and for examining the planet s response to the changes in the climate. As such, satellite instruments and their
measurements are playing an increasingly important role in supporting the formulation, management and stewardship of
environmental policies.
Starting with the first Earth Observation (EO) satellite TIROS-1 in 1960, a rigorous instrument calibration, a continuous
monitoring of sensor performance and an extensive validation of the derived products, using independent high-quality insitu measurements, or Fiducial Reference Measurements (FRM), have been key to any successful mission. Long-term data
quality requirements, such as for climate change or for data preservation, have added to the importance of the continuous
characterization of sensors and the validation of products across missions using FRM. In addition, the development in
recent years of downstream services using space-borne data have increased the need for documented end-to-end
characterization of the EO sensors and their derived products.
It is essential for all space agencies to ensure that an uninterrupted stream of high quality, well-calibrated FRM are available
to scientists and policy makers to address strategic issues of national and global significance. As far as calibration and
validation (Cal/Val), and data quality are concerned, many issues such as interoperability between EO sensors and products
have led to coordinated efforts in the Cal/Val methodologies through the Committee on Earth Observation Satellites
Working Group Cal/Val (CEOS/WGCV) and World Meteorological Organization s Global Space-based Inter-Calibration
System (WMO/GSICS), leading to the establishment of QA4EO guidelines by CEOS/WGCV and their subsequent adoption by
the Group on Earth Observation (GEO). These efforts need improved coordination across the space agencies and
international bodies, especially in regards to the Fiducial Reference Measurements infrastructure, measurement planning
and the use of best practises across common activities, such as in the SnowPEx project.
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ORAL SESSION: SNOW EXTENT PRODUCTSMon- .
Overview of snow extent products participating at SnowPEx and pre-processing steps
for intercomparison
Gabriele Bippusa, Thomas Naglera, Elisabeth Rippera, Richard Fernandesb, Sari Metsämäkic
a
ENVEO IT GmbH, Austria
bCCRS,
c Finnish
Canada
Environment Institute, Finland
For the SnowPEx Snow Extent (SE) products intercomparison the methods and protocols defined at the 1 st ISSPI Workshop
in July 2014 are applied. So far,
global/hemispheric/continental SE products provided by the contributing product
providers for selected periods are included in the intercomparison exercise. The SE intercomparison performed by ENVEO is
based on a pixel by pixel moving window approach, using statistical measures to describe the comparison results.
The intercomparison exercises include the total land cover, and additionally analyses of product performances for particular
surface classes, such as forest, mountain areas, etc. The SE product intercomparison is done for all products aggregated to 5
km and 25 km grid size, using the map projection EASE-GRID . .
In this presentation we explain the pre-processing steps applied to the various SE products, following the methods and
guidelines defined at the 1st ISSPI Workshop.
The Satellite Snow Products Intercomparison
and Evaluation Experiment
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Program
Mon- .
International Satellite Snow Products Intercomparison
Workshop
ISSPI-
Intercomparison of snow extent products - CCRS method and first results
Richard Fernandes a, Gabriele Bippusb, Sari Metsämäkic, Elisabeth Ripperb, Thomas Naglerb
aCCRS,
bENVEO
cFinnish
Canada
IT GmbH, Austria
Environment Institute, Finland
Intercomparison of global and continental scale satellite based snow extent (SE) product is required to identify outlier
products and to identify areas with low agreement for further assessment. SE intercomparison is complicated by
differences in: i) product definition as either binary snow extent (SEB) or snow cover fraction (SCF) ii) in map projection and
grid resolution and iii) in the area mapped on any given date. Previous work (e.g. GLOBSNOW) has identified suitable
comparison metrics for comparing either SEB or SCF. Here we provide a probabilistic framework for translating between
SEB and SCF to allow for the metrics to be applied to all products. The probabilistic framework also allows for comparing
large (e.g. 25km square) grid cells with multiple product pixels. Finally, the framework is extended to model the uncertainty
of SEB or SCF over a grid cell due to missing retrievals in the cell. The approach is applied to all provided SNOWPEX SE
products for two annual cycles and results are reported. Additionally results based on a non-probabilistic framework using
only completely mapped grid cells are reported for comparison.
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The Satellite Snow Products Intercomparison
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International Satellite Snow Products Intercomparison
Workshop
Program
MON-2
ORAL SESSION: SNOW EXTENT PRODUCTS-
Mon- .
Intercomparison of snow extent products - ENVEO method and first results
Gabriele Bippus, Elisabeth Ripper, and Thomas Nagler
ENVEO IT GmbH, Austria
Intercomparisons of global / hemispheric snow extent products are important for quality assessment of the products and
for interpretation of the climate trends of seasonal snow cover. In this presentation we introduce a protocol for
intercomparison
of
medium
resolution
snow
products
using
statistical
parameters
to
characterize
the
agreement/disagreement between the snow products. The protocol is based on previous validation and intercomparison of
ENVEO within the ESA GlobSnow-2 and the EU FP 7 CryoLand projects, and has been further improved for SnowPEx. Input
to the procedure are pre-processed snow extent products in EASE-GRID 2.0 projection aggregated to 5 km and 25 km. The
procedure calculates various statistical parameters from a pixel-by-pixel intercomparison, excluding all regions where one
of the products shows clouds, open water, polar night or invalid pixels. The protocol is applied to the overall area, but also
to specific land surface classes to investigate the performance of the products in different environments. Beside statistical
parameters we calculate the mean spatial distribution of the differences between 2 snow products over a certain time
period. We will present first results of the intercomparison of 9 global / hemispheric snow extent products available for
SnowPEX and will discuss the various methods for presenting the results.
The Satellite Snow Products Intercomparison
and Evaluation Experiment
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Program
Mon- .
International Satellite Snow Products Intercomparison
Workshop
ISSPI-
Landsat based snow extent reference data set and first validation results
Elisabeth Rippera, Gabriele Bippusa, Thomas Naglera, Christopher J. Crawfordb,
Sari Metsämäkic, Rune Solbergd, and Karl Rittgere
a ENVEO
b Cryospheric
c
IT GmbH, Austria
Sciences Laboratory, NASA/GSFC, USA
Finnish Environment Institute, SYKE, Finland
d
e
Norwegian Computing Center, Norway
National Snow and Ice Data Center, USA
At the 1st ISSPI workshop it was decided to validate the medium and low resolution snow extent (SE) products participating
in SnowPEx with high resolution snow maps from Landsat (LS) data. A data set of 459, predominately cloud free, scenes
from Landsat-5, Landsat-7 (SLC-ON) and Landsat-8 was selected and compiled by the SnowPEx team. The scenes are
acquired in different environments and climate zones at different seasons and solar illumination over the northern
hemisphere. They cover different land cover types like forest, mountainous regions or areas with different amount of open
water bodies. In order to avoid a bias in the snow detection by using only one algorithm, we selected 4 published
algorithms for generating the Landsat snow reference data set: The algorithms from Dozier and Painter (2004) and Klein et
al (1989) result in binary snow extent maps and the algorithms of Salomonson and Appel (2006) and the TMSCAG (Painter
et al.) provide fractional snow extent. The TMSCAG algorithm is based on Landsat surface reflectance products from USGS,
whereas the other 3 algorithms use Landsat L1T raw data provided by USGS as input. The pre-processing steps radiometric
calibration and topographic correction are applied on the L1T scenes. .The Landsat snow products from the various
algorithms are intercompared at full resolution to investigate the performance in snow detection. The high resolution
Landsat snow products are reprojected and aggregated to each of the map projection and resolution of the coarser
resolution global/hemispheric snow extent products. We will present the method and first results of the intercomparison of
Landsat snow maps from different algorithms and the validation of global/hemispheric SE products using aggregated
Landsat snow maps for selected regions.
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The Satellite Snow Products Intercomparison
and Evaluation Experiment
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ISSPI-
Mon- .
International Satellite Snow Products Intercomparison
Workshop
Program
SnowPEx in-situ data sets and first validation results
Sari Metsämäkia, Gabriele Bippusb, Kari Luojusc, Chris Derksend, and others
a
Finnish Environment Institute, SYKE, Finland
b ENVEO
c
IT GmbH, Austria
Finnish Meteorological Institute, Finland
d
Environmental Canada, Canada
The vast majority of the data represents point-wise observations made at weather stations but also path data (snow
transects) are available to some extent. The snow parameters include Snow Depth, Snow water equivalent and Snow Cover
Fraction, depending on a dataset. An overview of the available data sets is given.
Comparison of snow-extent (SE) products vs. in-situ observation follows the SnowPEx validation protocol. In the protocol,
the validation method is defined separately for SCF-products (CSF 0-100%) and binary (snow/non-snow) products. The
validation activity started with SCF products i.e. AVHRR Pathfinder (PATHF), GlobSnow SE (GLSSE), MOD10_C5 (M10C05),
CryoLand Pan-European SE product (CRYOL) and finally, SCAG. According to the protocol, Snow Cover Fraction from in-situ
observations are derived either by i) direct CSF observations (these data are available only from Finnish snow course
network and from RIHMI snow transects) or ii) from Snow Depth observations converted to CSF based on a statistical
correspondence derived from Finnish snow course data where both CSF and SD are observed. Most SE-products to be
validated in SnowPEx are binary snow/no-snow products. The validation protocol differs from that used for direct SCFproducts, so that the data provider-defined SCF threshold for judging a pixel as snow is applied to generate the
approximate SCF. In both cases, instead of pixel-to-pixel comparison of single SCF-values, probability functions of SCF are
employed to generate of random sample of for the validation window. These samples are the input to the calculation of
numerical validation measures. The first results for the validation are presented, the emphasis being in the fractional snow
products.
The Satellite Snow Products Intercomparison
and Evaluation Experiment
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Program
Mon- .
International Satellite Snow Products Intercomparison
Workshop
ISSPI-
NESDIS global automated satellite snow product: current status and planned upgrades
Peter Romanov
NOAA, USA
NESDIS Global Multi-sensor Automated Snow/Ice Maps (GMASI) have been operationally produced at NOAA since 2006.
The system combines information inferred from satellite observations in the visible/infrared spectral bands and from
microwave sensors to generate continuous, gap-free daily global maps of snow and ice cover distribution at the spatial
resolution of 4 km. In the current configuration the primary input to the system consists of the data from AVHRR
instrument onboard METOP satellites and SSMIS data onboard DMSP satellites. Daily automated maps of the snow cover
distribution are further processed to monitor hemisphere-wide and continental-scale snow cover extent as well as
corresponding anomalies in the snow extent. The GMASI product is routinely used as one of the primary inputs to NOAA
Interactive Multisensor Snow and Ice Mapping System (IMS) and to the retrieval system processing data from VIIRS and
OMPS sensors onboard SNPP satellite.
In the presentation we discuss the results obtained during the last years of the system exploitation, identify issues affecting
the accuracy of the product and outline the directions of the future system development. We compare the snow cover
distribution and snow extent estimates generated with the automated algorithm with similar estimates based on the NOAA
interactive snow and ice product. The results of comparison of satellite-based snow cover retrievals with observations of
snow depth at ground-based meteorological stations will be presented and discussed.
The focus of the near-future modifications of the automated system will be on increasing the spatial resolution of snow and
ice maps. This will be achieved by replacing AVHRR observations in the system by VIIRS data. Improvements in the snow
and ice mapping under cloudy conditions are associated with the use of AMSR2 data in addition to SSMIS.
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International Satellite Snow Products Intercomparison
Workshop
Program
Recent Advancements to the Interactive Multisensor Snow and Ice Mapping System
Sean Helfrich
United States Department of Commerce, NOAA, USA
Environmental awareness of snow is a key input to NOAA s National Centers for Environmental Prediction (NCEP) numerical
weather prediction (NWP) models. Current NCEP NWP models rely on the Interactive Multi-Sensor Snow and Ice Mapping
System (IMS) snow cover area and the US Airforce snow depth products as the primary source of information on snow
cover. Recent innovations in the IMS have added a 1km resolution output, new imagery and in-situ data sources, new
formats, and additional fields (snow depth, sea ice concentrations, sea ice thickness, and a date of last observation). All
historical formats were retained to promote continuity through time, while adding capacities required by NCEP and NOAA s
Arctic Action Plan. The new snow depth, ice thickness, and ice concentrations apply a weighted blending of derived and
analysed data sources from satellites and in-situ observations. Downscaling and interpolation techniques are applied to
refine observations to a 4km scale. The release of this data has demonstrated improved utility, but remains in needs of
improvements that will be forthcoming in the late 2015. Examples of snow applications and assessment of the analyses will
be provided and reviewed.
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Program
International Satellite Snow Products Intercomparison
Workshop
MON-3
ORAL SESSION: SNOW EXTENT PRODUCTS-
Mon- .
Standard MODIS snow cover products
ISSPI-
George A. Riggsa, Dorothy K. Hallb, Christopher A. Crawfordc, Nicolo E. DiGirolamoa
a
SSAI & NASA, USA
b
c
NASA, USA
ORAU & NASA, USA
Satellite Snow Products intercomparison and evaluation Exercise (SnowPEX) consists of an international team that will
evaluate and compare current global / hemispheric snow-cover products to quantify uncertainty of long-term trends in
snow-cover extent (SCE) and snow-water equivalent (SWE). This ESA-initiated project is a high priority for the Global
Cryosphere Watch. The MODIS Snow and Ice Mapping Project has contributed five years of MOD10A1 snow-cover extent
products in the format required by SnowPEX. Following receipt of the data files, a daily hemispheric product was mosaicked
by SnowPEx personnel to create a Climate Modeling Grid (CMG) – like product for the Northern Hemisphere at 500-m
resolution for a single day. This is an extraordinarily detailed snow map of the Northern Hemisphere and the first available
at such a high resolution. Validation will be accomplished using Landsat and other high-resolution imagery.
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The Satellite Snow Products Intercomparison
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Mon- .
International Satellite Snow Products Intercomparison
Workshop
Program
Evaluation of JXAM5 snow cover extent product using ground based snow depth
information and Landsat images
Masahiro Horia, Konosuke Sugiurab, Tomonori Tanikawaa, Teruo Aokic,
Masashi Niwanoc, and Hiroyuki Enomotod
a Japan
Aerospace Exploration Agency, Japan
b University
c
d
of Toyama , Japan
Meteorological Research Institute, Japan
National Institute of Polar Research, Japan
JXAM5 snow cover extent (SCE) product includes global snow cover extent at 5km spatial resolution at weekly or halfmonthly intervals from 1979 to 2014 (except for some months in 1980, 1981, 1994, 1995 and 2001) derived from the data
acquired with polar orbiting satellite optical sensor series of AVHRR and MODIS. The long-term SCE products will be
connected to SCE to be derived from the data of SGLI onboard JAXA s Earth observing satellite GCOM-C to be launched in
around early 2017. The accuracies of the snow cover extent product were evaluated using snow depth stored in Global
Historical Climate Network-Daily (GHCND) provided by NOAA/NCDC. As the precedent studies have indicated, omission
errors of the JXAM5 SCE tend to increase in spring melting season at dense forest areas such as closed broadleaved
deciduous forest and needle-leaved evergreen forest and so on which are identified using the GlobCover land cover
product. For the improvement of the SCE in such dense vegetation cover regions, capability of snow cover detection in
dense forest area is under evaluation for domestic snow sites (Hokkaido in Japan) using in-situ snow depth, UAV photos,
and high resolution Landsat imageries. In addition, long-term trends of annual snow cover duration period derived from the
JXAM5 half-monthly SCE product are being evaluated and compared with those derived from the GHCND data at around
the ground station sites.
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Assessment of H-SAF MSG/SEVIRI snow cover product over the UK
Samantha Pullen
Met Office UK, United Kingdom
At the Met Office, a snow assimilation scheme is under development for implementation in the high resolution UK
forecasting model. Both ground station snow depth reports and satellite-derived snow cover data will be used to produce
an improved analysis of model snow extent and amount. Assessment of the snow cover product from the EUMETSAT H-SAF
suggests that it would be a suitable source of UK snow cover data in the new assimilation scheme. This paper will describe
the assessment of the H-SAF snow cover product for potential assimilation over the UK, and introduce the new assimilation
scheme under development.
The lack of widespread persistent snowy conditions in the UK makes validation of observational and model snow data hard.
Validation of satellite-derived snow cover data depends upon there being sufficient ground station reports, and cloud-free
satellite observations for meaningful analysis. Validation must, therefore, be performed using data from exceptionally
snowy winters. The UK experienced two episodes of severe winter weather between late November and late December
2010. Significant snowfalls on both occasions, and persistently low temperatures, led to much of the UK spending
considerable time under lying snow, making this a particularly useful study period for assessment of modelled and observed
snow in the UK.
For evaluation of the H-SAF snow cover product, comparisons of snow presence have been performed between the H-SAF
product, short-range forecast fields of snow amount from the UK NWP model, and ground-based observations of snow
from the UK Synoptic network, during December 2010. Representation of snow cover compared well between satellite
product and model, with an overall rate of agreement of over 80%. An estimate of the relative accuracies of the remotely
sensed and modelled snow cover was found by comparing both datasets with a common set of ground-based snow
observations. Rates of agreement with ground station observations were very high for both datasets, but the H-SAF product
performed better overall, with an agreement rate of over 89%. These results, along with qualitative studies of the snow
cover in each dataset on individual days, indicate the added value that the H-SAF data could provide by assimilation into the
forecasting model.
This paper will present full results of the assessment study, noting examples of particularly good, or poorer, performance.
Some of the difficulties of validating satellite snow products are well illustrated in this study, and these will be discussed,
along with some of the current activities underway to address them.
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Assessment of the stability of a satellite snow extent CDR from station snow depth
observations
David Robinsona, Thomas Moteb, and Kim Love-Myersb
a
b
Rutgers University, USA
University of Georgia, USA
Station and satellite-based measures of snow cover extent produce complementary, but occasionally conflicting, climate
data records (CDRs). A record of snow cover extent from visible satellite data is available on a weekly basis since the late
1960s, while a gridded station product for North America is available on a daily basis for the past century. Several changes
in the visible satellite record have been documented by the Rutgers Global Snow Lab. The effects of these discontinuities in
the satellite CDR — such as different sensors, overpass times, or mapping methodologies — have not been fully assessed.
Here the gridded North American snow depth record is compared to the visible satellite record for different epochs within
the satellite CDR. The average snow depths in the station product are identified for the 50th percentile probability of snow
identification in the satellite product. The data set was broken into three different time periods, 1965 to 1980 (Period 1),
1981 to 1998 (Period 2), and 1999 to 2009 (Period 3), corresponding roughly to periods of technological and processing
changes in the NOAA snow charts used to generate the CDR. A logistic regression modeled the probability of snow cover
detection in any satellite cell based on two variables: average snow depth reported from weather stations, and fraction of
reporting stations reporting at least 1 cm of snow. The modeled results were used to adjust the earlier two periods based
on the most recent period, and the adjustment indicated that most recent period shows 5.23% greater snow extent Period
1, and 2.75% more than Period 2.
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Time series analysis of mountain snow cover from MODSCAG and VIIRSCAG
ISSPI-
Kathryn Bormanna, Karl Rittgerb, and Thomas. H., Paintera
a
b
Jet Propulsion Laboratory, USA
National Snow and Ice Data Center, USA
The continuation of existing large-scale snow cover records into the future is crucial for monitoring the impacts of global
pressures such as climate change, weather variability and dust deposition on the cryosphere. With daily MODIS records
since 2000 from a now ageing MODIS constellation (Terra & Aqua) and daily VIIRS records since 2012 from the Suomi-NPP
platform, the coherence of information between the two optical sensors for long-term monitoring must be reconciled. Daily
fractional snow and vegetation cover data from SCAG (Snow Cover and Gran Size) algorithms were collected for both
MODIS (Terra) and VIIRS retrievals and used to derive long-term estimates of regional snowline elevation and intra-season
anomalies in snow covered area in mountainous terrain. The study focuses on snowline elevation in an attempt to capture
changes in the rain/snow transition zone where climate change pressures may be enhanced. We also focus on snow cover
area anomalies to both expand examination of snow cover metrics beyond the typical snow cover area and snow cover
duration, with the expectation that we can start to capture extreme events (as the spatial datasets become of sufficient
length). Where intra-seasonal snowfall extremes are important at the regional scale and may have implications for
avalanche and flooding risk. Despite the large inter-annual variability often observed in snow metrics, we expect that over
the 15-year time series we will see a rise in elevation of the snowline and the rain/snow transition boundary. However, the
15-year record is not expected to be sufficient to capture maximum snowfall extremes amidst significant inter-annual
variability further demonstrating the importance of maintaining our earth observing records into the long-term. This work
combines recent advances in snowline detection algorithms and the application of existing spectral unmixing techniques for
fractional snow detection from VIIRS retrievals to create multi-sensor time series for long-term trend analysis.
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ORAL SESSION: SNOW EXTENT PRODUCTS-
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Advancements and validation of the global CryoClim snow cover extent product
Rune Solberga, Øystein Rudjorda, Arnt-Børre Salberga, and Mari Anne Killieb
a
Norwegian Computing Center, Norway
b MET
Norway, Norway
Changes in the snow cover over time are a sensitive indicator of climate change at the global, continental and local scale.
The Arctic region is warming faster than the Earth in general, and the corresponding part of the cryosphere is significantly
affected. This includes the seasonal snow cover resulting in shorter snow seasons.
There are rather long time series (> 30 years) of daily optical and passive microwave radiometer (PMR) data of global
coverage available. However, both sensor types are sensitive to weather conditions. Optical data can only be used under
cloud-free conditions and with the sun sufficiently high above the horizon. With PMR it is difficult to retrieve the snow
cover accurately when the snow is wet or when the snow depth is shallow.
To mitigate such effects we have in the CryoClim project (www.cryoclim.net) developed a multi-sensor multi-temporal
algorithm for retrieval of snow cover extent (SCE) at the global scale from AVHRR GAC plus SMMR and SSM/I satellite data.
The data from the two sensor types are fused in a multi-sensor model and analysed in a time series of observations. The
resulting product is a binary SCE map of 5 km resolution. The algorithm is applied for generating a ~30-year time series of
daily snow maps that will be updated regularly with new observations.
While the initial validation showed high accuracy in general (92.4%), there were a few known weaknesses that needed to
be mitigated, as the accuracy requirements for a climate product are strict. Additionally, a more comprehensive validation
was needed as to better characterise the temporal and land-cover dependent accuracy. In situ data from the former Soviet
Union is by several authors in the snow research community regard as the data of highest quality available, in particular the
HSDSD Version 2 and the FSUHSS data sets. HSDSD has two major advantages: its quality control and the fact that snow
depth is recorded year-round, even during the warm season when snow is absent. This makes it possible to accurately and
unambiguously identify the onset and end of the periods with snow on the ground, in contrast to the regular WMO synoptic
observations, which are affected by occasional coding errors and sometimes do not distinguish between lack of snow and
missing observations. The HSDSD data set starts in 1881 with a few stations and ends in 1995. The FSUHSS dataset includes
snow density, snow depth and SWE observations generally collected within one day, 3 times per month, along transects.
The presentation will explain the algorithm and the characteristics of the CryoClim snow product, the new improvements
implemented and the validation analysis and results.
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POSTER PRESENTATIONS (ORAL, 2 MINUTES PER POSTER)
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Validating a new snow cover extent mapping algorithm over Eastern Canada
ISSPI-
Sophie Robergea, Karem Chokmania, Danielle De Sèveb
a Institut
b
National de la Recherche Scientifique, Canada
Institut de Recherche d'Hydro-Québec, Canada
Snow cover is a key element in the hydrological cycle of Eastern Canada. Being the leader in electricity supply, monitoring
daily snow cover extent (SCE) and snow water equivalent (SWE) is one of the highest priorities for Hydro-Quebec to predict
snowmelt water incomes. In the last five years, an operational procedure have been developed by both INRS and IREQ s
remote sensing research teams to maps daily SCE over the provinces of Quebec and Labrador (Eastern Canada). The SCE
mapping procedure is made of six thresholds to detect snow-covered areas, snow-free areas and clouds from 1-km daily
NOAA-AVHRR images (visible and thermal infrared) taken during onset of snow cover and snowmelt periods, over the years
1988 to 2013. Snow maps have been compared with ground-truth snow measurements. The preliminary validating results
have shown an overall accuracy of 95%. SCE has been identified in 90% of the time over Eastern Canada. However, SCE has
been identified in 65% of the time only in forested areas. The SCE mapping procedure needs to be improved.
The objective of this study is to develop and validate a new procedure to improve monitoring of daily snow cover extent
over Eastern Canada, more precisely in dense forest areas located in the southern part of the study area. Data included in
the procedure are: (1) 1985-2005 AVHRR-1km and 2000-2011 MODIS-250m land cover products, both developed by CCRS
(Canada Center for Remote Sensing), and (2) 32km NARR reanalysis of surface air temperature. Unlike the previous SCE
mapping procedure, this one includes threshold values depending on air temperature, and for land cover classes. Validation
includes various datasets: (1) a set of high resolution LANDSAT images covering 18 locations in various land cover over the
study area, most of them are in the boreal forest; (2) 1988-2013 daily snow depth measurements from 20 stations of
Environment Canada snow depth network over the study area; (3) 2005-2009 daily snow depth measurements from 16
stations of Hydro-Québec snow depth network located in the Gatineau basin; (4) 2007-2011 near-real time snow water
equivalent from 7 stations of Hydro-Québec GMON network located in the Cascades basin.
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Satellite based probabilistic snow cover extent mapping (SCE) at Hydro-Québec
Danielle De Sèvea, Teasdale Mylèneb, Jean-Francois Angerb, and Luc Perreaulta
a
Institut de Recherche d'Hydro-Québec IREQ, Hydro-Québec, Canada
b Département
de mathématiques et de statistique, Université de Montréal, Canada
Over 40% of Canada's water resources are in Quebec and Hydro-Quebec has developed potential to become one of the
largest producers of hydroelectricity in the world, with a total installed capacity of 36,643 MW. The Hydro-Québec fleet
park includes 27 large reservoirs with a combined storage capacity of 176 TWh, and 668 dams and 98 controls. Thus, over
98% of all electricity used to supply the domestic market comes from water resources and the excess output is sold on the
wholesale markets.
In this perspective the efficient management of water resources is needed and it is based primarily on a good river flow
estimation including appropriate hydrological data. Snow on ground is one of the significant variables representing 30% to
40% of its annual energy reserve. More specifically, information on snow cover extent (SCE) and snow water equivalent
(SWE) is crucial for hydrological forecasting, particularly in northern regions since the snowmelt provides the water that fills
the reservoirs and is subsequently used for hydropower generation.
For several years Hydro Quebec s research institute (IREQ) developed several algorithms to map SCE and SWE. So far all the
methods were deterministic. However, given the need to maximize the efficient use of all resources while ensuring
reliability, the electrical systems must now be managed taking into account all risks. Since snow cover estimation is based
on limited spatial information, it is important to quantify and handle its uncertainty in the hydrological forecasting system.
This paper presents the first results of a probabilistic algorithm for mapping SCE by combining Bayesian mixture of
probability distributions and multiple logistic regression models applied to passive microwave data. This approach allows to
assign for each grid point, probabilities to the set of the mutually exclusive discrete outcomes: "snow" and "no snow". Its
performance was evaluated using the Brier score since it is particularly appropriate to measure the accuracy of probabilistic
discrete predictions. The scores were measured by comparing the snow probabilities produced by our models with the
Hydro-Québec's snow ground data.
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Fractional snow cover for Landsat OLI, MODIS, and VIIRS from spectral mixture
analysis
Karl Rittgera, Kathryn Bormannb, Richard, Armstrongc, Thomas, Painterb, and Jeff Dozierd
a
National Snow and Ice Data Center, USA
b Jet
Propulsion Laboratory, USA
c NSIDC/CIRES,
d
USA
University of California Santa Barbara , USA
Maps of snow from MODIS have seen growing use in investigations of climate, hydrology, and glaciology. Some of these
snow cover maps have been validated with ground measurements that represent points, not areal extents. Alternatively
the snow cover maps have been compared to higher spatial resolution satellite sensors such as Landsat 5, TM and Landsat
7, ETM+ that saturate in visible wavelengths over snow where models need information to separate snow from other land
surfaces. In addition, cubic convolution used in processing satellite data has made identifying saturated pixels virtually
impossible and it mixes saturated pixels with unsaturated ones. Snow mapping models can have errors in either the positive
or negative direction depending on assumptions and performance with lowered visible reflectance (ie saturation). Snow
cover estimates from Landsat TM/ETM+ using spectral mixture analysis assume 100% snow cover when bands 1, 2, and 3
saturate, so it likely overestimates snow cover fraction systematically whereas a band ratio would likely underestimate
snow cover for saturated pixels. Fortunately, the recently launched Landsat 8 OLI sensor can provide an areal estimate of
fractional snow cover at 30m and has a dynamic range that does not saturate in the visible wavelengths, increasing the
accuracy of the model above implementations used for previous comparisons and providing an opportunity to better
understand MODIS snow cover maps as well as newer VIIRS snow cover maps.
We apply spectral mixture analysis to satellite data from Landsat OLI, MODIS, and VIIRS. Fractional snow cover maps from
MODIS and VIIRS have similar spatial resolution of 500m and 1km respectively. We compare these maps to the 30m spatial
resolution fractional snow maps from Landsat OLI. For the comparison we coarsen the 30m maps as well as the 500m and
1km maps to the half the spatial resolution to account for geolocation errors and calculate binary and fractional statistics.
The comparison of MODIS to OLI shows that underestimates of snow cover from MODIS (-2%) are smaller than previously
estimated using Landsat TM/ETM+ (-7%). The smaller difference in the negative direction supports the hypothesis that
saturation in Landsat TM/ETM+ overestimated snow cover and reveals that the biases from MODIS fractional snow cover at
~500m are smaller then previously thought. The comparison of VIIRS to OLI shows slightly higher biases of - % and larger
root mean squared errors of 16% compared to 11% from MODIS. The difference is largely driven by lower recall or
probability of detection likely associated with the coarser 1km VIIRS data.
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Scene-specific fractional snow cover algorithms for optical remote sensing
Igor Appela
a
NOAA/STAR IMSG, USA
The future snow applications of satellite data to a great degree depend on using the Visible Infrared Radiometer Suite
(VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) platform. Because of an original scanning and
aggregation scheme, VIIRS observations providing the spectral coverage from 412 nm to 12 μm are characterized by a pixel
growth factor of only two both along a track and along a scan giving the opportunity of getting snow distribution globally at
800 m resolution and 375 m at nadir.
The information from visible and near-infrared bands is very useful to distinguish snow from non-snow since snow is among
the brightest of natural substances in the visible part of the spectrum, but it is also often the darkest in the short wave
infrared. At the same time, the snow and background reflectance spectra exhibit significant variability changing in time and
from one place to other.
The distinction between the reflectance in the visible wavelengths and the reflectance in the near infrared wavelengths is
characterized by the Normalized Difference Snow Index (NDSI) widely considered as an indicator of the presence of snow
on the ground.
It has been previously established that NDSI has the following advantages

NDSI is sensitive enough to provide the snow fraction

NDSI presenting relative ratio of reflectances to a large degree suppresses the influence of varying illumination
conditions
A linear relationship between snow fraction (SF) and observed NDSI proposed by Salomonson and Appel is equivalent to a
linear interpolation between snow and non-snow NDSI:
SFNDSI = (NDSI - NDSInon-snow) / (NDSIsnow - NDSInon-snow) Changes in pixel reflectances should not be ascribed
exclusively to variable fraction, because they depend also on the variability in spectral signatures of the endmembers. The
quality of snow retrieval could be improved if the variability of reflective properties characterizing snow and underlying
non-snow states is taken into account.
Allowing for the variability in spectral signatures of endmembers is a key requirement to snow algorithms. When a scenespecific approach detects local endmembers (on the fly) the comparison of fractional snow retrieval with ground truth is
characterized by high quality. Average correlation coefficient is 94% despite a couple of low magnitudes. Typical intercept
of linear regression line is on the order of 1%. Average slope of linear regression line is more than 0.9. Average bias of data
is %. Average standard deviation (uncertainty) is 10%.
The adjustment of the parameters in snow algorithms to specific local conditions is a promising improvement leading to
better quality of the VIIRS snow products.
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The seasonality of snow/cloud discrimination in Landsat imagery across the Northern
Hemisphere
Christopher Crawford
NASA, USA
Snow/cloud discrimination in optical satellite imagery is well understood across the solar spectrum. Even so, frequent cloud
obstruction of the visible snow surface across geographic regions with persistent atmospheric instability requires a
steadfast commitment to characterize automated cloud-screening errors during the snow season. Imagery from the
Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager/Thermal Infrared
Sensor (OLI/TIRS) are playing a fundamental role in Satellite Snow Product Intercomparison and Evaluation Experiment
(SnowPEx) validation activities. While most of the Landsat validation images have either minimal cloud contamination or
are cloud-free, a number of images require pixel-based cloud screening prior to snow cover classification. In this work, a
seasonally representative subsample of SnowPEx Landsat images are selected from the Northern Hemisphere domain, and
three different Landsat cloud/cloud shadow algorithms, Function of Mask, Landsat Ecosystem Disturbance Adaptive
Processing System (LEDAPS), and heritage automated cloud cover assessment (ACCA) are evaluated for their performance.
Seasonal biases and the challenges of differentiating between liquid, mixed-phase, and ice cloud types are identified for
Landsat-type spectral and seasonal resolutions, and trade-offs of cloud/cloud shadow mask choice are discussed. Several
physically based improvements in Landsat cloud screening are explored using thermal brightness temperatures, the water
vapour spectral band carried on OLI, and hyperspectral cloud retrievals from the EO-1 Hyperion imaging spectrometer.
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Evaluating multi-spectral snowpack reflectivity with changing snow grain sizes
Do Hyuk Kanga, Ana Barrosb, and Edward Kima
a NASA
b Duke
GSFC, USA
University, USA
This study investigates the sensitivity of multi-spectral reflectivity to changing snow grain size. To this end, this paper
implements and tests Mätzler s Ice-lamellae model to evaluate the reflectivity of snow grain sizes at multiple frequencies
from the ultraviolet (UV) to the microwave bands. The model reveals that the reflectivity is inversely proportional to
increasing grain size in the UV to infrared (IR) frequency range. On the other hand, in the microwave frequency range, the
reflectivity increases with snow grain size. The model further shows that the reflectivity behaviour can be mainly attributed
to scattering rather than absorption. The largest scattering coefficients and reflectivity occur with very small grain sizes
(~10-5 m) at high frequencies above the IR band. At microwave frequencies, the largest scattering coefficients are found at
mm wavelengths. For validation purposes, the Ice-lamellae model is coupled with a multi-layer snow physics model to
match reflectivity responses with realistic snow hydrological processes. Simulated reflectivities from the coupled model in
both the visible and the microwave bands are consistent with satellite-based reflectivity data. The model results are also
compared with co-located in-situ snow grain size measurements (Cold Land Processes Field Experiment 2002-2003). This
coupled modelling system to cover multiple frequencies can provide a critical tool to improve a forward operator in a data
assimilation framework to predict the status of snow physical properties including snow grain size.
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Optimal linear relationships of fractional snow cover with visible reflectance and NDSI
Igor Appela
a
NOAA/STAR IMSG, USA
It is considered a standard approach to apply high-resolution observations to validate moderate resolution retrieval of the
fractional snow cover using the aggregation of high-resolution pixels binary classified as snow / non-snow. It is also typical
that the algorithms to retrieve fractional snow cover are based on processing visible and near infrared reflectances.
The quality of remote sensing observations is comparable for different sensors including both high-resolution and moderate
resolution. It means that there is no principal difference between reflectances calculated from different sensors. However,
the estimates of moderate resolution fractional snow cover retrieval quality are influenced to a large degree by varying
viewing geometry and the observations from nadir are therefore preferable. Since moderate-resolution observations on
reflectances are approximately equal to aggregated high-resolution data, the latter (reflectances and ground truth snow
fraction) are quite sufficient to validate and improve fractional snow cover algorithms.
16 Landsat scenes characterized by a wide variety of surface types and solar illumination conditions were taken into
consideration to implement proposed methodology. The quality of two algorithms based on assumptions of linear
relationships of visible reflectance and the Normalized Difference Snow Index (NDSI) with snow fraction was estimated
using the regressions of ground truth fraction on both visible reflectance and NDSI.
The comparison of two algorithms demonstrates obvious advantages of the regression on NDSI (characterized by
correlation coefficients of 0.95) when compared to the linear regression on the visible reflectance (characterized by
correlation coefficients of 0.85).
Further analysis indicates approximately 60% increase in the standard deviation for the regression of the fractional snow
cover on visible reflectance in comparison with the standard deviation for the regression on NDSI in the case when the
intercept for the regression lines is set to zero. It means that the variance for the regression on the visible reflectance is
twice larger than the variance for the regression on NDSI.
The uncertainty of the relationships under consideration is less than 0.1 in most cases for the regression on NDSI, but only
in two Landsat scenes for the regression on visible reflectance. The worst quality of the regression on NDSI is characterized
by the standard deviation of less than 0.12, but more than 0.20 for the regression on visible reflectance.
The comparison above has been made for optimal linear relationships of fractional snow cover with visible reflectance and
NDSI. However it is not clear how the realization of the visible band algorithm could be optimized and therefore practically
the retrieval based on the visible reflectance provides much poorer quality of snow fraction than described above. The
scene-specific realization of the NDSI algorithm on the contrary is close to its optimal version and could meet requirement
to the VIIRS snow fraction retrieval.
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Snow monitoring in mountain areas from medium to high resolution images: towards
Sentinel 2 data
Carlo Marina, Edoardo Cremoneseb, Ludovica De Gregorioa, Mattia Callegaric, and Claudia Notarnicolaa,c
a
b
EURAC, Italy
Arpa Valle d'Aosta, Italy,
cUNiversitá
di Pavia, Italy
This paper presents two snow cover products derived from the 250 meters MODIS and the 30 meters LANDSAT-8 data. An
extensive validation have been carried out that considers: i) ground data; ii) a cross-comparison between the two proposed
products; and iii) a cross-comparison with the standard MODIS product MOD10-MYD 10. The proposed 250 meters
resolution snow cover product comes from an improved version of the EURAC snow cover algorithm based on 250 m
MODIS satellite images, which was specifically designed for mountain areas (Notarnicola et al., 2013). In the improved
version of the algorithm, a specific module for the proper detection of snow in forest has been introduced. This module
automatically downscales the information derived from NDSI index and keeps the resolution of the final map at 250 m. The
agreement between standard MODIS snow cover product (i.e. MOD10-MYD 10) and the snow maps obtained by the
proposed methods considering a pool of dates from 2003 to 2013 increases of around 7-8% inside forests, meanwhile the
misclassification decreases of around 3-4% outside forests. Additionally, in order to have a further and thorough validation,
the snow maps from 2005 to 2009 have been compared with ground data, which have been collected by 83 snow depth
sensors located in Valle d'Aosta and Piemonte (Northern Italy). Elevation, forest cover and morphology (i.e. 3 categories:
ridges, slopes and valley floor) has been considered for each validation point, in order to understand the impact of these
parameters on the accuracy of the snow maps. Differently from the MODIS data, in LANDSAT 8 images more spatial details
are visible. From one hand this allows the discrimination among a higher number of land cover types, nonetheless on the
other hand it increases the complexity of the detection among the classes. Indeed, if standard strategies based on static
thresholds are applied the generated decision trees may become complex (i.e., large) and the correct evaluation of
uncertain values may fail. For this reason in this work we propose to use a supervised support vector machine (SVM)
classifier. SVM has the intrinsic potentiality to find non-linear discriminant functions in a multi-dimensional feature space
(Camps-Valls&Bruzzone, 2005). The main features used for the classification are: i) the LANDSAT 8 spectral bands; ii) the
physically inspired features extracted as combination of bands (e.g., NDSI) that model the proprieties of snow, clouds and
vegetation; and iii) the topographic characteristics of the investigated mountain region. The obtained results have been
compared with the results obtained by standard approaches based on static thresholds showing that the proposed method
is more accurate especially when similar classes (e.g., clouds and snow) or mixed classes (e.g., snow under forest) have to
be identified. It is finally worth noting that the capability to extract information from medium and high resolution data is of
great importance in view of the availability of free-of-charge Sentinel 2 data. In this context LANDSAT 8 can be considered a
precursor of Sentinel 2 and their data can be used in order to explore the potentially of the upcoming Sentinel 2 images.
References
Camps-Valls, G.; Bruzzone, L., Kernel-based Methods for Hyperspectral Image Classification, IEEE Transactions on Geoscience and Remote Sensing, Vol. 43, No.
,
,
-
.
Notarnicola, C.; Duguay, M.; Moelg, N.; Schellenberger, T.; Tetzlaff, A.; Monsorno, R.; Costa, A.; Steurer, C.; Zebisch, M. Snow Cover Maps from MODIS Images
at 250 m Resolution, Part 1: Algorithm Description. Remote Sens. 2013, 5, 110-
.
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Updated JAXA GCOM-W/AMSR2 snow depth standard product and its validation
Hiroyuki Tsutsuia, Keiji Imaokaa, Richard Kellyb, and Takashi Maedaa
a Japan
Aerospace Exploration Agency (JAXA), Japan
b
University of Waterloo, Canada
The JAXA GCOM-W/AMSR2 standard products were updated in Japan fiscal year 2014, and the following products were
opened to the public: 1) water vapour, 2) cloud liquid water, 3) precipitation, 4) SST, 5) sea surface wind speed, 6) sea ice
concentration, 7) snow depth, and 8) soil moisture content. Among these products, the AMSR2 snow depth standard
product was improved by Dr. Richard Kelly at University of Waterloo. This improvement is scientifically-important in order
to achieve the AMSR2 goal accuracy of ±10cm snow depth. The following major improvement was implemented in this
product: (a) correction of the brightness temperature based on a forest transmissivity, (b) application of an estimation
methodology for a realistic snow depth distribution over the Tibetan Plateau and (c) update of spatial water fraction data.
In the improvement (a), a forest correction expression is derived using the MODIS reflectance data to estimate forest
transmissivity according to the work of Metsämaki et al (2005). The forest correction factor for each frequency is applied to
correct for forest attenuation. This is a significant improvement because the impact of forest affects the estimation of snow
depth by the microwave remote sensing strongly. In the improvement (b), the microwave estimation methodology, which
can estimate the sparse snow distribution over the Tibetan Plateau, was applied and was evaluated by snow cover area
based on ALOS/AVNIR-2 with 10 m high spatial resolution. Furthermore, the water fraction data was updated using the
WWF GIS dataset and the product also detects the presence of frozen or liquid water state. Thereafter, the estimated snow
depth from this product was compared with in situ snow depth observations from the WMO GSOD data set consisting of
1007 stations in the northern hemisphere. The mean average error (MAE) is calculated for spatially and temporallymatched stations within a 30 km diameter of the AMSR2 snow depth estimate. Validation period is from July 23, 2012 to
September 30, 2014. The AMSR2 snow depth standard product achieved the release and standard accuracies, which are 20
cm in MAE. In the workshop, further detail for these improvements and validations and future subject will be presented.
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Mon- .
International Satellite Snow Products Intercomparison
Workshop
Program
Using image reconstruction to enhance spatial resolution of a reprocessed satellite
passive microwave historical record
Mary J. Brodzika, David L. Longb, Molly A. Hardmanc, Aaron Pagetb, and Richard L. Armstrongc
a
National Snow and Ice Data Center, USA
b
c
BYU/MERS, USA
CU/CIRES/NSIDC, USA
Currently available global gridded passive microwave data sets serve a diverse community of hundreds of data users, but do
not meet many requirements of modern Earth System Data Records (ESDRs) or Climate Data Records (CDRs), most notably
in the areas of intersensor calibration and consistent processing methods. The original gridding techniques were relatively
primitive and were produced on 25 km grids using the original EASE-Grid definition that is not easily accommodated in
modern software packages. Further, since the first Level 3 data sets were produced, the Level 2 passive microwave data on
which they were based have been reprocessed as Fundamental CDRs (FCDRs) with improved calibration and
documentation. We are funded by NASA MEaSUREs to reprocess the historical gridded data sets as EASE-Grid 2.0 ESDRs,
using the most mature available Level 2 satellite passive microwave (SMMR, SSM/I-SSMIS, AMSR-E) records from 1978 to
the present. We are currently producing prototype data from SSM/I and AMSR-E for the year 2003, for review and feedback
from our Early Adopter user community. The prototype data set includes grids derived from the two candidate image
reconstruction techniques we are evaluating: 1) Backus-Gilbert (BG) interpolation and 2) a radiometer version of
Scatterometer Image Reconstruction (SIR). We discuss our rationale for the respective algorithm tuning parameters we
have selected, compare results and computational costs, and include prototype sample images at enhanced resolutions of
up to 3 km. We anticipate that using our new brightness temperature ESDR may improve current snow water equivalent
products in at least three ways: 1) we are using the latest intersensor calibration from Level 2 input data sets, 2) we are
tuning image reconstruction parameters to enhance gridded spatial resolution, and 3) we will be producing consistently
gridded image products for the complete sensor record (SMMR, AMSR-E, 6 SSM/Is and at least 2 SSMISs).
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International Satellite Snow Products Intercomparison
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COST Action ES1404: A European network for a harmonised monitoring of snow for the
benefit of climate change scenarios, hydrology and numerical weather prediction
Samantha Pullen
Met Office UK, United Kingdom
A new COST Action on snow was initiated in November 2014 and will run for four years. The Action is chaired by Dr Ali Nadir
Arslan, from FMI, and has a Management Committee made up of representatives from 24 European countries. The Action
brings together snow scientists from diverse applications in participating countries and will achieve its aims by networking
activities including working groups, workshops, and training opportunities, focusing on three main issues: (1) Physical
characterisation of snow properties, (2) Instrument and method evaluation, and (3) Snow data assimilation and validation
methods for NWP and hydrological models. The primary objective of the Action is to enhance the capability of the research
community and operational services to provide and exploit quality-assured regional and global observation-based data on
the variability of the state and extent of snow. Secondary objectives are:

Establish a European-wide science network on snow measurements and their optimum use and applications, by
direct inclusion and interactions across disciplines and expertise.

Assess and harmonise practices, standards and retrieval algorithms applied to ground, air-borne and space-borne
snow measurements and foster their acceptance by key snow network operators at the international level.

Develop a rationale and long-term strategy for snow measurements, their dissemination and archiving.

Advance the application of snow data assimilation in European NWP and hydrological models and show its benefit
for weather and hydrological forecasting as well as other applications.

Establish a validation strategy for NWP, hydrological and climate models against snow observations and advance
its implementation within the European modelling communities.

Training of a new generation of scientists on snow science and measuring techniques with a broader and more
holistic perspective linked with the various applications.
This presentation will give an overview of the Action, explaining its structure, goals and how these will be achieved over the
four year period.
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International Satellite Snow Products Intercomparison
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TUE-1
TUESDAY, 15 SEPTEMBER 2015
ORAL SESSION: SNOW WATER EQUIVALENT-
Tue- .
The Satellite Snow Product Intercomparison and Evaluation Experiment: overview of
progress for snow water equivalent
Chris Derksena, Lawrence Mudrykb, Kari Luojusc, Ross Browna, Carrie Vuyovichd
a
Environment Canada, Canada
b University
c
d
of Toronto, Canada
Finnish Meteorological Institute, Canada
Cold Regions Research and Engineering Laboratory, USA
Reliable information on snow water equivalent (SWE) at regional to continental scales is required for studies of freshwater
and energy budgets, the evaluation and initialization of land surface models for both short term weather and seasonal
forecasts, and the assessment of coupled climate model simulations. While multiple independent passive microwave
derived SWE products are available, their full potential has not been realized because of poorly constrained error budgets
due to the challenges related to the physical processes underpinning the SWE retrievals, and the extensive snow covered
regions of the world without adequate surface observations for algorithm validation. The purpose of SnowPex is to obtain a
quantitative understanding of the uncertainty in remotely sensed SWE products through an internationally coordinated and
consistent evaluation exercise.
This presentation will provide an overview of initial SnowPEx progress related to the inter-comparison and evaluation of
SWE products, including:

the use of high quality networks of independent ground reference measurements,

the methods for scaling surface observations and for taking account of sampling biases e.g. open areas and lower
elevations,

the role of gridded SWE products from reanalysis and land surface models,

the calculation of SWE trends over the passive microwave satellite record, and comparison with the reference
gridded products.
Open issues related to these three areas will also be discussed, in order to develop consensus from workshop participants
on how best to proceed.
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Assessment of the satellite-based SWE datasets with snow transect data
Kari Luojusa, Jouni Pulliainena, Chris Derksenb, Ross Brownb
a Finnish
Meteorological Institute, Finland
b Environment
Canada, Canada
The SnowPEx team has acquired the currently available satellite-based SWE datasets and performed the preliminary
intercomparison of the different products. The inter-comparison has been carried out for the 1) GlobSnow SWE, 2) the
NASA Standard SWE, 3) NASA prototype SWE and 4) HSAF SWE products.
The intercomparisons have been carried out by using ground-based snow course observations as the reference. The
evaluations have been carried out using Finnish Snow Course data, available for 1979-2014, covering Finland and
distributed snow transect data collected from the former Soviet Union and Russia covering the period 1979–2012. The
reference dataset contains snow path measurements carried out within 517 different snow path locations, ranging from 35°
to 85° northern latitude and 14° to 179° of eastern longitude. The preliminary results will be shown at the SnowPEx ISSPI-2
workshop.
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The Satellite Snow Products Intercomparison
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Tue- .
International Satellite Snow Products Intercomparison
Workshop
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Evaluating the consistency and accuracy of several northern hemisphere snow water
equivalent datasets
Lawrence Mudryka, Chris Derksenb, Paul Kushnera, and Ross Brownb
a
University of Toronto, Canada
b
Environment Canada, Canada
We analyse spatial and temporal consistency and accuracy within and between two classes of daily, gridded Northern
Hemisphere snow water equivalent (SWE) datasets over two recent periods.
We first present a completed analysis of several reanalysis-based SWE products over the 1981-2010 period: 1) the Global
Land Data Assimilation System (GLDAS); 2) the Crocus distributed physical snow model driven by meteorology from ERAInterim reanalysis; 3) ERA-Interim/Land reanalysis; and 4) Modern Era Retrospective Analysis for Research and Applications
(MERRA reanalysis). We find that the climatologies of total Northern Hemisphere snow water mass (SWM) vary among the
datasets by as much as 50%, but their interannual variability and daily anomalies are comparable, showing moderate to
good temporal correlations (between 60% and 85%) on both interannual and intraseasonal time scales. Examining spatial
patterns of SWE indicates that the datasets are most consistent with one another over boreal forest regions compared to
Arctic and alpine regions. Wintertime trends of total Northern Hemisphere SWM are consistently negative over the 19812010 period among the datasets but vary in strength by a factor of 2- . We also highlight temporal inhomogeneities present
in other prominent SWE datasets: the MERRA-Land reanalysis, the Canadian Meteorological Centre Daily Snow Depth
Analysis and an earlier version of the GLDAS product. Finally, we provide evidence that while land surface model differences
control the majority of spread in the climatological value of SWM, meteorological forcing differences control the majority of
the spread in temporal correlations of SWM anomalies.
We will contrast the comparison of reanalysis-based products with that of three satellite-based products over the 20032010 period: 1) the GlobSnow combined satellite passive microwave analysis; 2) the standard AMSR-E operational product
and 3) a new prototype of the AMSR-E operational product. Where possible we will also provide an initial assessment of
accuracy of both the reanalysis- and satellite-based products with recently available high quality in situ observations.
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Development of a 10-km gridded SWE dataset over south-western Quebec for
SnowPEx validation
Ross Browna and Dominique Tapsobab
a
b
Environment Canada, Canada
L'Institut de recherche d'Hydro-Québec (IREQ), Canada
As a contribution to SnowPEX, co-kriged estimates of SWE at a 10-km resolution were generated over a region of southwestern Quebec from in situ snow course data collected by Hydro-Québec and partners from 1999 to 2013 for the 1st and
15th of the month over the Jan-April survey period. This particular area was selected because of a relatively even
distribution of snow course surveys and the presence of several passive gamma GMON SWE observing systems in the
region. The kriging methodology is based on Tapsoba et al (1999) and uses up to five variables to optimize the spatial model
(latitude, longitude, elevation, land use, and estimated SWE from the Canadian Meteorological Centre daily snow depth
analysis). The talk will outline the datasets and methods used in developing the gridded SWE estimates, present crossvalidation results, and compare the new H-Q dataset with SWE estimates from GlobSnow and MERRA. Preliminary results
suggest that these products considerably under-estimate SWE over the region.
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Tue- .
International Satellite Snow Products Intercomparison
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Watershed-based comparison of global SWE algorithms to SNODAS modelled SWE
across the United States
Carrie Vuyovich, Jennifer Jacobs, Timothy Baldwin, and Steven Daly
ERDC, USA
The Satellite Snow Product Intercomparison and Evaluation Experiment (SnowPEX) seeks to compare and validate several
passive microwave satellite-based snow water equivalent (SWE) algorithms as well as the SWE output from global climate
models. Accurate SWE data is essential for water resource applications in many regions that rely on snowmelt for water
supply and are susceptible to flood and drought. For this study we evaluate the various SWE products by comparison to the
NOAA NOHRSC Snow Data Assimilation System (SNODAS) spatially distributed model estimates. Results are compared for
over 2,000 HUC8 watersheds throughout the United States. This watershed comparison allows us to evaluate data across
multiple regions and provides an independent means for validation of both the satellite and modelled results by
comparison to basin discharge. We use watersheds selected from the Hydro-Climatic Data Network (HCDN), which
identifies basins with minimal human impacts to stream flow. A previous comparison of SNODAS to SSM/I and AMSR-E
standard algorithms found that lack of observations in the Central Plains region negatively impacts the SNODAS results both
on magnitude and timing of SWE and passive microwave data has potential for providing an accurate source of SWE data in
this region. In regions with relatively deep snow packs and heavy vegetation, the satellite estimates suffered. This study
evaluates the additional SWE products made available from SnowPEx; AMSR-E prototype algorithm and GlobSnow by
comparison to SNODAS and the standard algorithms. Results are evaluated by several basin statistics to determine the
difference in magnitude. The Spearman s rank-order test is used to compare the relative magnitudes of annual maximum
estimates. Weekly SWE data are compared using the Nash-Sutcliffe model efficiency index which measures the fit between
predicted and observed values. Global climate model estimates of SWE are similarly compared to SNODAS, but at the HUC4
basin scale because of the coarse resolution of the data.
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TUE-2
ORAL SESSION: SNOW WATER EQUIVALENT-
Tue- .
An overview of the current NASA operational AMSR-E/AMSR2 snow science team
activities
Marco Tedescoa, Mattia Sartorib, and Jeyavinoth Jeyaratnama
a The
City College of New York (CCNY), USA
b CCNY
/ University of Brescia, Italy
The knowledge of snow depth (SD) and snow water equivalent (SWE) is fundamental for water resources management and
weather and flash flood forecast, among other things. Snow also strongly influences the global energy balance because of
its high reflectivity, insulation properties, and the amount of latent heat consumed during melting. Data collected at global
scale by space-borne passive microwave sensors have been used to estimate SWE and SD at operational level. Data
collected by the Advanced Microwave Scanning Radiometer 2 (AMSR2) and by the Advanced Microwave Scanning
Radiometer for EOS (AMSR-E) are routinely used to generate global maps of SWE and SD. In this paper, we focus on
providing an overview of some of the activities of the NASA operational AMSR-E/AMSR2 snow science team activities. In
particular, we discuss the following tasks:

Adapting the AMSR-E SWE algorithm to for AMSR2 to ingest L1R brightness temperature provided by JAXA

Assessing the improvement of dynamic snow density masks on SWE

Updating the Snow Possible/Snow Impossible mask using the CMC dataset.

Developing of a research prototype algorithm

Comparison between current operational algorithm and research prototype algorithm and in-situ/modelled snow
depth fields
Page

Evaluation of an enhanced spatial resolution product with in situ measurements

Cross-calibration between AMSRE and AMSR2 brightness temperatures
The Satellite Snow Products Intercomparison
and Evaluation Experiment
nd
ISSPI-
Tue- .
International Satellite Snow Products Intercomparison
Workshop
Program
The GCOM-W1 AMSR2 snow depth and snow water equivalent product
Richard Kelly, Nastaran Saberi, and Qinghuan Li
University of Waterloo, Canada
Estimates of snow depth (SD) and snow water equivalent (SWE) are presented from recent developments of the standard
snow product for the Advanced Microwave Scanning Radiometer – 2 (AMSR2) aboard the Global Change Observation
Mission – Water. AMSR2 is designed as a follow-on from the successful Advanced Microwave Scanning Radiometer – EOS
that ceased formal operations in 2011. The standard SD product for AMSR2 has been updated in two ways. First, the
detection algorithm identifies various observable geophysical targets that can confound SD / SWE estimation (water bodies
[including freeze/thaw state], rainfall, high altitude plateau regions [e.g. Tibetan plateau]) before detecting moderate and
shallow snow. Second, the implementation of the Dense Media Radiative Transfer model (DMRT) originally developed by
Tsang et al. (2000) and more recently adapted by Picard et al. (2011) is used to estimate SWE and SD. The implementation
combines snow grain size and density parameterizations originally developed by Kelly et al. (2003). Snow grain size is
estimated from the tracking of estimated air temperatures that are used to drive an empirical grain growth model. Snow
density is estimated from the Sturm et al. (2010) scheme. Efforts have been made to keep the approach tractable while
reducing uncertainty in these input variables. Results are presented from the recent winter seasons to illustrate the
performance of the new approach in comparison with the current AMSR2 algorithm.
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International Satellite Snow Products Intercomparison
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Contributions of SnowPEx to global climate modelling initiatives
Chris Derksen (with contributions from the LS3MIP and ESM-SnowMIP steering committees)
Climate Research Division, Environment Canada, Canada
Model intercomparison projects ('MIPs') provide an important benchmarking opportunity to assess the performance of
specific components of climate models, and diagnose sources of uncertainty. Two MIPs which include consideration of
terrestrial snow cover are currently under development: the Land Surface, Snow, and Soil Moisture (LS3MIP) proposed to
the Coupled Model Intercomparison Project phase 6 (CMIP6), and the Earth System Model Snow MIP (ESM-SnowMIP)
endorsed by the World Climate Research Programme (WCRP) Climate and Cryosphere (CliC) initiative. Both of these
initiatives require a well validated set of gridded snow cover extent (SCE) and snow water equivalent (SWE) datasets in
order to quantify and model biases and support process studies. This presentation will provide an update on the status of
LS3MIP and ESM-SnowMIP focusing on an overview of the proposed simulations and corresponding observational
requirements. In the case of both SCE and SWE, SnowPEx is poised to provide the observational foundation for the analysis
of the model simulations, and will provide a new understanding of observational spread and uncertainty for comparison
with the inter- (between model) and intra- (between ensemble member) model spread.
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International Satellite Snow Products Intercomparison
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The NASA SnowEx airborne snow campaign: an upcoming opportunity for SnowPEx
Edward Kima, Charles Gatebeb, Dorothy Hallb, Matthew Sturmc
a
NASA, USA
b NASA
c
GSFC, USA
UAF, USA
NASA is planning a 3-year airborne snow campaign notionally called SnowEx, and planned for the northern hemisphere
winters of 2016-
. The primary goal of SnowEx is the collection of coincident observations with a suite of observation
types including active and passive optical and active and passive microwave sensors. Detailed ground truth will also be
collected for algorithm verification, accuracy comparisons, and quantitative assessments of SWE, SCA, SCF, and depth
measurement uncertainties under a variety of conditions and snow types, etc. Also being discussed are studies to
quantitatively assess the distribution of uncertainties between actual measurements vs. models when these are combined
in an assimilation framework to produce snow data products.
The objective of this presentation is to inform the SnowPEx community of SnowEx plans, to begin exploring the potential
for SnowPEx to exploit SnowEx activities, and to offer the opportunity for SnowPEx lessons-learned concerning validation
issues to be incorporated into the design of the SnowEx campaign to the benefit of future global-scale snow satellite data
products.
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WED-1
WEDNESDAY, 16 SEPTEMBER 2015
WED- .
The role of product intercomparisons in the Global Cryosphere Watch
ISSPI-
Jeff Key
National Oceanic and Atmospheric Administration (NOAA), Madison, Wisconsin, USA
The World Meteorological Organization (WMO) Global Cryosphere Watch (GCW) is a sustained, robust, end-to-end
operational cryosphere observing and monitoring system that encompasses ground-based measurements, satellite remote
sensing, aircraft measurements, modelling, and data management. GCW provides authoritative, clear, and useable data,
information, and analyses on the past, current and future state of the cryosphere. But what is authoritative information?
In order to address this question, at least in part, GCW is promoting and engaging in product intercomparisons studies. The
goal of a product intercomparison is to evaluate dataset maturity, determine in-depth error characteristics, assess strengths
and limitations, resolve discrepancies, provide information on dataset availability, and develop guidelines for product
improvement. The GCW Snow Watch Team and the European Space Agency s (ESA) SnowPEx project are engaged in an
intercomparison of snow products, including satellite-derived, in situ, and analysis/reanalysis products. Lessons learned
from SnowPEx, as well as from intercomparison projects underway by the International Cloud Working Group and the
International Winds Working Group, will help determine the best methodology to employ in intercomparison projects for
other cryosphere variables, such as a sea ice thickness.
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The Satellite Snow Products Intercomparison
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